1. Aim 4 : Economics and Policy

2. Aim 4 Members Robert Abt, Faculty, NCSU Damian Adams, Faculty, UF Douglas Carter, Faculty, UF Don Grebner, Faculty, MSU Jianbang Gan, Faculty, TAMU Gary Peter, Faculty, UF Mike Kane, Faculty, UGA Tom Fox, Faculty, VT Francisco Escobedo, Faculty, UF Wendell Cropper, Faculty, UF Puskar Khanal, Student, MSU Soyoung Lee, Student, TAMU Robert Abt, Faculty, NCSU Damian Adams, Faculty, UF Douglas Carter, Faculty, UF Don Grebner, Faculty, MSU Jianbang Gan, Faculty, TAMU Gary Peter, Faculty, UF Mike Kane, Faculty, UGA Tom Fox, Faculty, VT Francisco Escobedo, Faculty, UF Wendell Cropper, Faculty, UF Puskar Khanal, Student, MSU Soyoung Lee, Student, TAMU

3. Comprehensive life cycle analyses of regional forest management systems Multi-scale policy and economic analysis of market and non-market forest benefits and services Evaluate regional tradeoffs and interactions among policy, climate scenarios, carbon/water /nutrient/energy footprints, forest management, and genetic deployment Assess adoption of alternative approaches by private landowners. Objectives

4. Key linkage to Other Aim Groups Aim 4 Conducts economic and Policy studies Aim 1 Aim 5/6 Aim 3 Aim 2 Provides field-level data to support LCA and regional modeling Provides data and forest growth/yield modeling results, and carbon dynamics to support LCA and regional modeling Provides findings on the impact of selected genotypes on productivity and resistance to disturbances to support LCA and regional modeling Extension, Interaction with research efforts for collaborative working relation and partnership

5. Relation to PINEMAP goals Increase C sequestration by 15 % by 2030; Increase efficiency of practices by 10% by 2030; Increase forest resilience and sustainability under variable climates Aim 1: Provide field-level data to support LCA and regional modeling Aim 2: provide data and modeling results on forest growth /yield and carbon dynamics Aim3: Provide results on the Impact of selected genotypes on forest productivity and resistance to disturbances Aim 4: Conduct LCA analysis, optimization studies, financial and policy analysis,and landowner studies Aim 5/6: Create educational resources and training materials, deliver state-of –art information to landowners, resource managers and policy makers

6. Broader approaches to look at aim 4 objectives: Approach 3. Scenario analysis and modeling Aim 4 approaches 1. Life cycle analysis (LCA) 2. Financial and policy analysis 4. Landowner studies

7. Approach/method forest productsforestry operations 1. Life cycle analysis (LCA) Is a “cradle-to-grave” approach for assessing industrial systems.

8. How would other groups assist? Providing data on each stage of a product's life from cradle to grave (from raw material extraction through materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling); and modeling results 1. Life cycle analysis (LCA) Key question addressed Potential environmental impacts associated with the identified inputs and releases, or processes Approach

9. 2. Financial/economic and policy analysis These tools are employed for determining the optimum use of scarce resources, involving comparison of two or more alternatives in achieving a specific objective under the given assumptions and constraints Major financial analysis tools Net present value analysis, Faustmann formula, Hartman formula, discounted cash flow analysis Policy review and analysis tax policy, subsidies effect analysis

10. Approach 2. Financial/economic and policy analysis Key question answered Potential financial returns from the alternatives Expected economic and policy environment to make the practice acceptable to the landowners How would other aim groups assist? Providing data about the adoption practices (aim group 5 & 6) of the selected genotypes (aim group 3) predicted to provide certain growth and yield (aim group 3) under varied climactic conditions (aim group 1)

11. The major approaches for scenario modeling and analysis ⇒ Options analysis coupled with dynamic optimization under risk ⇒ Panel data modeling ⇒ Sub regional Timber Supply (SRTS) modeling Approach 3. Scenario analysis and modeling

12. Dynamic modeling/optimization Represents a technique for the systematic determination of optimal combinations of decisions Is a method for numerically solving a dynamic system of equations Can work iteratively through the sequence of decisions in forward or backward flowing fashion Represents a technique for the systematic determination of optimal combinations of decisions Is a method for numerically solving a dynamic system of equations Can work iteratively through the sequence of decisions in forward or backward flowing fashion Approach Is used to maximize biological potential and economic returns related to a stand of trees 3. Scenario analysis and modeling

13. Panel data modeling Panel data refers to pooled cross-section and time series, longitudinal, or repeated measurements Approach Panel data model could reveal, Impact of climate change on the risk of the disturbances Contributions of adaptations to mitigating the risk under climate change Economic losses in terms of timber and carbon values based on timber and carbon prices and projected future forest areas in the South under climate change. Panel data model could reveal, Impact of climate change on the risk of the disturbances Contributions of adaptations to mitigating the risk under climate change Economic losses in terms of timber and carbon values based on timber and carbon prices and projected future forest areas in the South under climate change. 3. Scenario analysis and modeling

14. SubRegional Timber Supply (SRTS) modeling SRTS uses FIA data to project timber supply trends based on current conditions and the economic responses in timber markets It models regional product market and carbon consequences of business-as-usual and potential new silvicultural management regimes It uses the empirical product supply and demand relationships to project changes in inventory characteristics (forest type, age class structure, product inventories, harvest, and growth) SRTS uses FIA data to project timber supply trends based on current conditions and the economic responses in timber markets It models regional product market and carbon consequences of business-as-usual and potential new silvicultural management regimes It uses the empirical product supply and demand relationships to project changes in inventory characteristics (forest type, age class structure, product inventories, harvest, and growth) Approach 3. Scenario analysis and modeling

15. How would other aim groups assist? Providing data on projected management scenarios on different climatic conditions ( aim 1), their genome and productivity characteristics ( aim 3), their growth and yield models ( aim 2), and the landowner level feedback about optimum practices (aim 5 & 6) How would other aim groups assist? Providing data on projected management scenarios on different climatic conditions ( aim 1), their genome and productivity characteristics ( aim 3), their growth and yield models ( aim 2), and the landowner level feedback about optimum practices (aim 5 & 6) Approach 3. Scenario analysis and modeling Key questions answered Optimum management scenarios with given biological potential and expected economic return Economic analysis, risks involved, and climate change impact Projected demand and supply situations under different climactic conditions and timber management scenarios Key questions answered Optimum management scenarios with given biological potential and expected economic return Economic analysis, risks involved, and climate change impact Projected demand and supply situations under different climactic conditions and timber management scenarios

16. Approach 4. Landowner studies The major tools used in landowner studies are Survey tools Landowner socio-economic studies, decision making, and willingness studies Their Behavior modeling and econometric analysis Key questions answered Landowner characteristics, forestry behaviors and preferences Their willingness to adopt optimum management scenarios, and predicted forestry practices, and policy environment desired for adoption of new regimes

17. Thank you !!